Automatic retraining of machine learning models to detect DDoS attacks

ABSTRACT

In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/356,023, filed on Jun. 29, 2016, entitled “AUTOMATIC RETRAINING OFMACHINE LEARNING MODELS TO DETECT DDoS ATTACKS, by Reddy, et al., thecontents of which are herein incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to automatically retraining machine learning models todetect distributed denial of service (DDoS) attacks.

BACKGROUND

Enterprise networks are carrying a very fast growing volume of bothbusiness and non-business critical traffic. Often, business applicationssuch as video collaboration, cloud applications, etc., use the samehypertext transfer protocol (HTTP) and/or HTTP secure (HTTPS) techniquesthat are used by non-business critical web traffic.

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests, to prevent legitimaterequests from being processed. A DoS attack may also be distributed, toconceal the presence of the attack. For example, a distributed DoS(DDoS) attack may involve multiple attackers sending malicious requests,making it more difficult to distinguish when an attack is underway. Whenviewed in isolation, a particular one of such a request may not appearto be malicious. However, in the aggregate, the requests may overload aresource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIGS. 3A-3E illustrate an example architecture for mitigating a networkattack;

FIGS. 4A-4D illustrate examples of reducing the false positive rate of anetwork attack detector;

FIGS. 5A-5D illustrate examples of reducing the false negative rate of anetwork attack detector; and

FIG. 6 illustrates an example simplified procedure for the retraining ofan attack detection model.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device in anetwork receives an attack mitigation request regarding traffic in thenetwork. The device causes an assessment of the traffic, in response tothe attack mitigation request. The device determines that an attackdetector associated with the attack mitigation request incorrectlyassessed the traffic, based on the assessment of the traffic. The devicecauses an update to an attack detection model of the attack detector, inresponse to determining that the attack detector incorrectly assessedthe traffic.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local networks 160, 162 that include devices/nodes 10-16and devices/nodes 18-20, respectively, as well as a data center/cloudenvironment 150 that includes servers 152-154. Notably, local networks160-162 and data center/cloud environment 150 may be located indifferent geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise, illustratively,an attack detection/mitigation process 248, as described herein.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Attack mitigation process 248 includes computer executable instructionsthat, when executed by processor(s) 220, cause device 200 to performattack detection and mitigation functions as part of an attack detectionand mitigation infrastructure within the network. One type of networkattack that process 248 may detect and mitigate is a Denial of Service(DoS) attack. In general, the goal of a DoS attack is to preventlegitimate use of the services available on the network. For example, aDoS jamming attack may artificially introduce interference into thenetwork, thereby causing collisions with legitimate traffic andpreventing message decoding. In another example, a DoS attack mayattempt to overwhelm the network's resources by flooding the networkwith requests (e.g., SYN flooding, sending an overwhelming number ofrequests to an HTTP server, etc.), to prevent legitimate requests frombeing processed. A DoS attack may also be distributed, to conceal thepresence of the attack. For example, a distributed DoS (DDoS) attack mayinvolve multiple attackers sending malicious requests, making it moredifficult to distinguish when an attack is underway. When viewed inisolation, a particular one of such a request may not appear to bemalicious. However, in the aggregate, the requests may overload aresource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

In various embodiments, attack mitigation process 248 may employ machinelearning, to detect and/or mitigate network attacks. In general, machinelearning is concerned with the design and the development of techniquesthat receive empirical data as input (e.g., traffic data regardingtraffic in the network) and recognize complex patterns in the inputdata. For example, some machine learning techniques use an underlyingmodel M, whose parameters are optimized for minimizing the cost functionassociated to M, given the input data. For instance, in the context ofclassification, the model M may be a straight line that separates thedata into two classes (e.g., labels) such that M=a*x+b*y+c and the costfunction is a function of the number of misclassified points. Thelearning process then operates by adjusting the parameters a,b,c suchthat the number of misclassified points is minimal. After thisoptimization/learning phase, attack mitigation process 248 can use themodel M to classify new data points, such as information regarding newtraffic flows in the network. Often, M is a statistical model, and thecost function is inversely proportional to the likelihood of M, giventhe input data.

Example machine learning techniques that attack mitigation process 248can employ may include, but are not limited to, nearest neighbor (NN)techniques (e.g., k-NN models, replicator NN models, etc.), statisticaltechniques (e.g., Bayesian networks, etc.), clustering techniques (e.g.,k-means, mean-shift, etc.), neural networks (e.g., reservoir networks,artificial neural networks, etc.), support vector machines (SVMs),logistic or other regression, Markov models or chains, principalcomponent analysis (PCA) (e.g., for linear models), multi-layerperceptron (MLP) ANNs (e.g., for non-linear models), replicatingreservoir networks (e.g., for non-linear models, typically for timeseries), or the like.

Notably, Denial of Service (DoS) attacks are relatively easy to detectwhen they are brute-force (e.g. volumetric), but may be difficult todistinguish from a flash-crowd (e.g., an overload of the system due tomany legitimate users accessing it at the same time), when highlydistributed. This fact, in conjunction with the increasing complexity ofperformed attacks, makes the use of “classic” (usually threshold-based)techniques unable to detect such attacks. Machine learning techniques,however, may still be able to detect such attacks, before the network orservice becomes unavailable. For example, some machine learningapproaches may analyze changes in the overall statistical behavior ofthe network traffic (e.g., the traffic distribution among flow flattenswhen a DDoS attack based on a number of microflows happens). Otherapproaches may attempt to statistically characterizing the normalbehaviors of network flows or TCP connections, in order to detectsignificant deviations. Classification approaches try to extractfeatures of network flows and traffic that are characteristic of normaltraffic or malicious traffic, constructing from these features aclassifier that is able to differentiate between the two classes (normaland malicious).

Distributed DoS (DDoS) attacks present unique challenges to a network.In some cases, the system may use DDoS Open Threat Signaling (DOTS), tocoordinate defensive measures among willing peers, to mitigate attacksquickly and efficiently. An overview of the requirements of a DOTSsystem is provided in the Internet Engineering Task Force (IETF) Draftentitled, “Distributed Denial of Service (DDoS) Open Threat SignalingRequirements,” by Mortensen et al., which is hereby incorporated byreference.

The following terminology is typically used with respect to a DOTSsystem:

-   -   Attack Target—the server, service, or application under DDoS        attack.    -   DOTS Client/Attack Detector—a software module/network node        responsible for detecting DDoS attacks and using DOTS signaling        to request mitigation, etc.    -   DOTS Server—a software module/network node responsible for        communicating with DOTS clients and coordinating mitigation        actions.    -   DOTS Mitigator—a node (e.g., a network element) that is able to        perform mitigation actions on attack traffic (e.g., by dropping        the traffic, etc.).    -   DOTS gateway: A logical DOTS agent resulting from the logical        concatenation of a DOTS server and a DOTS client, analogous to a        SIP Back-to-Back User Agent (B2BUA).    -   DOTS Agent—any function element in a DOTS system including DOTS        clients, servers, etc.

Referring now to FIGS. 3A-3E, an attack mitigation architecture 300 isshown, according to various embodiments. As shown, architecture 300 mayinclude a data center 150 or other local network connected to anAnti-DDoS service 302 via CE router 110, CE-1. Service 302 may be, forexample, a cloud-based service, a service offered by a service provider,or any other service through which traffic directed towards data center150 flows. Notably, service 302 may include any number of routers orother networking nodes/devices 40-45 that direct traffic towards CErouter 110.

Also as shown, architecture 300 may implement DOTS, to defend server 152in data center 150 against network attacks, such as DDoS attacks.Accordingly, data center 150 may also include a DDoS Detector/DOTSclient 304 that may be executed on CE-1 or another device incommunication therewith. In general, DOTS client 304 is configured toassess traffic to and/or from data center 150, to detect potentialnetwork attacks. For example, DOTS client 304 may use a machinelearning-based model, to determine whether the traffic is potentiallyrelated to an attack. In some cases, DOTS client 304 may also beconfigured to attempt to perform local mitigation of an incoming attackdetected by DOTS client 304 (e.g., by dropping traffic, etc.). However,for a large-scale attack, DOTS client 304 may not have the resources tofully mitigate the attack.

As part of architecture 300, DOTS client 304 may be in communicationwith a DOTS server 306 using DOTS signaling, either directly through thenetwork or via a DOTS relay (not shown). DOTS server 306, in turn, maybe in communication with, and provide supervisory control over, anynumber of DOTS mitigator(s) 308 configured to perform attack mitigationfunctions.

As shown in FIG. 3B, consider the situation in which a DDoS attack isunderway. For example, attack traffic 310 may be routed towards theattack target, server 152 from any number of distributed attack nodesand via any number of routing paths. In such a case, DOTS client 304 maydetect that an attack is underway and send a DOTS signal 312 to DOTSserver 306 requesting attack mitigation.

In turn, as shown in FIG. 3C, DOTS server 306 may send signal(s) 314 toDOTS mitigator(s) 308 and/or the network devices associated therewith,to divert attack traffic 310 towards DOTS mitigator(s) 308. Generally,DOTS mitigator(s) 308 may also be configured to assess network trafficto discern attack traffic and take any number of mitigation actions onthe attack traffic. For example, while dropping the attack traffic isone possible mitigation strategy that may be employed by DOTSmitigator(s) 308, other strategies can be used in other implementations.

In FIG. 3D, node 40 and/or any other nodes signaled by DOTS server 306may divert the indicated attack traffic 310 to DOTS mitigator(s) 308. Aswould be appreciated, node 40 may itself execute a DOTS mitigator 308,allowing node 40 to perform the analysis locally, in some cases. Inother cases, node 40 may divert attack traffic 310 to another devicehosting a DOTS mitigator 308.

In turn, as shown in FIG. 3E, DOTS mitigator(s) 308 may assess theattack traffic 310 (e.g., using its own attack detection functions), andpotentially using deeper analysis techniques than that of DOTS client304 (e.g., a more robust attack detector, using techniques such as deeppacket inspection, etc.). If DOTS mitigator(s) 308 then determine thatan attack is present, based on the analysis of traffic 310, DOTSmitigator(s) 308 may perform any number of mitigation actions on traffic310, thereby protecting server 152 from the attack. For example, a DOTSmitigator 308 may act as a Transport Layer Security (TLS) proxy, inspectthe payloads of packets, detect and block attack traffic, etc. In somecases, DOTS mitigator(s) 308 may also provide feedback regarding theattack to DOTS client 304 and/or to DOTS server 306.

As noted above, a DDoS detector, such as DOTS client 304, may uselightweight mechanisms that passively monitor traffic, leveragingsignatures and machine learning techniques to detect DDoS attack. Forexample, one type of DDoS detector may monitor IP Flow InformationExport (IPFIX) and/or Netflow records, to detect DDoS attacks. Anotherdetector type may instead monitor whether incoming traffic is cloned ormirrored, to detect DDoS attacks. Further, if the payload of theincoming traffic is encrypted, then the DDoS detector can only rely onmachine learning techniques to detect L7 DDoS attacks (like Slowlorisattacks).

Machine learning-based DDoS detection has several advantages oversignature based DDoS detection. In particular, machine learningtechniques can detect Layer 7 (L7) DDoS attacks on encrypted flows,detect deviations from the baseline traffic (e.g., using a trafficmodel), and detect new/unknown attacks. Also, machine learning-basedDDoS detectors are able to detect L7 attacks with a high degree ofaccuracy. However, such techniques are not infallible and can, in somecases, raise false alarms (i.e., false positives). In addition, as aperfect attack detector is often not achievable, the detector mayoccasionally “miss” the detection of an attack and label attack trafficas benign (i.e., a false negative).

Automatic Retraining of Machine Learning Models to Detect DDoS Attacks

The techniques herein propose that when a lightweight DDoS detectorraises a false alarm indicating that a DDoS attack is in progress, orfails to detect an actual DDoS attack, a heavyweight DDoS mitigatorautomatically retrains the machine learning model of the lightweightDDOS detector without human intervention, to reduce false positives andfalse negatives. Such a lightweight DDoS detector may simply use machinelearning for purposes of attack detection/mitigation, whereas theheavyweight DDoS mitigator may perform additional functions, such asdecrypting packets with the necessary keys and examining packet payloadsfor L7 attacks.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with process248, which may include computer executable instructions executed by theprocessor 220 (or independent processor of interfaces 210) to performfunctions relating to the techniques described herein.

Specifically, according to various embodiments, a device in a networkreceives an attack mitigation request regarding traffic in the network.The device causes an assessment of the traffic, in response to theattack mitigation request. The device determines that an attack detectorassociated with the attack mitigation request incorrectly assessed thetraffic, based on the assessment of the traffic. The device causes anupdate to an attack detection model of the attack detector, in responseto determining that the attack detector incorrectly assessed thetraffic.

Operationally, FIGS. 4A-4D illustrate examples of reducing the falsepositive rate of a network attack detector, in accordance with variousembodiments. As shown in FIG. 4A, consider the operations of the DOTSagents 304-308 from FIGS. 3A-3E when the lightweight attack detector,DOTS client 304 may take the following actions:

-   -   1. DOTS client 304 monitors traffic to detect DDoS attacks on        server 152.    -   2. DOTS client 304 detects an L7 DDoS attack and conveys a DOTS        signal to DOTS 304 server, to request attack mitigation.    -   3. DOTS server 306, in turn, instructs DOTS mitigator 304 to        mitigate the attack.    -   4. Attack traffic destined to server 152 is diverted towards        DOTS mitigator 304, such as via the border gateway protocol        (BGP) or domain name system (DNS) protocol.    -   5. DOTS mitigator 304 acts as transparent TLS proxy, inspects        the payload, and detects and blocks attack traffic.

Now, assume that DOTS client 304 raised a false alarm (e.g., a falsepositive), meaning that an attack on server 152 is not underway. Basedon the above functionality, DOTS mitigator 304 may perform its ownanalysis of the traffic re-diverted to mitigator 304 before taking anymitigation actions. As noted previously, mitigator 304 may employ moreheavyweight analysis techniques than that of DDoS detector/DOTS client304, to determine whether an attack is underway. For example, mitigator304 may perform packet inspection on the traffic, decrypt encryptedtraffic, use a more computationally-expensive machine-learning model, oremploy any other traffic analysis functions that were not performed byDOTS client 304.

As shown, if DOTS mitigator 304 determines that DOTS client 304 issued afalse alarm (e.g., based on its own analysis of the traffic), mitigator304 may send a message 402 back to DOTS server 306 indicating thatclient 304 issued a false alarm. In turn, DOTS server 306 may covey toDOTS client 304 that mitigator 304 determined that the raised alarm wasa false alarm and that no attack is in progress via message 404.

In various embodiments, as shown in FIG. 4B, mitigator 304 may alsoupdate the parameters of the machine learning mechanism of DDoSdetector/DOTS client 304, in response to determining that client 304raised a false alarm. In one embodiment, mitigator 304 may use anoptimization approach such as stochastic gradient descent with the newlabeled data (e.g., the traffic data flagged as benign by mitigator304), to update the parameters of the detection model. In turn,mitigator 304 may send the machine learning process, its parameters,and/or the newly labeled data to DDoS detector/DOTS client 304 via oneor more messages 406. Message(s) 406 may be sent either directly toclient 304 or indirectly to client 304 through DOTS server 306, asshown.

In FIG. 4C, DDoS detector/DOTS client 304 may update its attackdetection model using the information received via message(s) 406 frommitigator 304. In one embodiment, client 304 may retrain its machinelearning model(s) using labeled data received from mitigator 304 viamessage(s) 406 (e.g., the traffic data that has been labeled benign bymitigator 304). In another embodiment, client 304 may modify theparameters of its machine learning process using parameters receivedfrom mitigator 304. In further embodiments, client 304 may train a newattack detection model using the labeled data included in message(s)406.

In some cases, as shown in FIG. 4D, the organization making use of DOTSmay also agree to share the labeled data from mitigator 304 withauthorized third parties. Consider, for example, the case in which thereare any number of DDoS detectors/DOTS clients 304 a-304 n (e.g., a firstthrough nth detector) operated by any number of different entities. Insuch cases, mitigator 304 can convey the labeled data via messages 406to any number of the different clients 304 a-304 n either directly inother networks or indirectly via DOTS server 306. For privacy reasons,mitigator 304 may only share labeled encrypted data outside of theoriginal organization. In addition, mitigator 304 may share DDoS attackdetails with interested and authorized third parties.

Referring now to FIGS. 5A-5D, example techniques are shown to reduce thefalse negative rate of a network attack detector, according to variousembodiments. Generally, false negatives occur when the attack detectorincorrectly determines that attack traffic is benign. In the specificcase of DOTS, this means that the local DDoS detector/DOTS client 304will not signal DOTS server 306 for attack mitigation when a falsenegative occurs, since client 304 believes the traffic to be harmless.

In various embodiments, any number of authorized network resources maybe configured to act as a DOTS client for purposes of signaling DOTSserver 306 for attack mitigation. For example, as shown in FIG. 5A,consider the case in which the target server 152 is itself a DOTSclient. In such a case, server 152 may make its own determination as towhether or not an attack is underway. For example, if the user logintime on server 152 has increased significantly, this may indicate thatan attack is underway. In turn, server 152 may send message 502 to DOTSserver 306 to request attack mitigation.

Since any network device can act as a DOTS client, this also gives wayto other mechanisms to identify when DDoS detector 304 produced a falsenegative. In another embodiment, a user operating a user interface inthe network may signal that a potential attack is underway, based on hisor her own assessment of the operation of server 152. For example, if auser reports that server 152 is unavailable, an administrator mayoperate a DOTS client-enabled web portal to convey a mitigation requestto DOTS server 504.

Regardless of the source of the mitigation request from a device otherthan DDoS detector 304 (e.g., server 152, a user interface, etc.), DOTSserver 306 may initiate mitigation in its normal manner. For example, asshown in FIG. 5B, DOTS server 306 may instruct mitigator 304 to mitigatethe reported attack via an instruction 504. In turn, the attack trafficdestined for target server 152 may be diverted towards mitigator 304(e.g., using BGP or DNS messaging).

Once the traffic is sent to mitigator 304, mitigator 304 may perform itsown analysis of the traffic, to determine whether the traffic is trulypart of an attack. For example, mitigator 304 may act as a transparentTLS proxy to decrypt encrypted payloads, perform DPI to inspect trafficpayloads and take corrective measures if an attack is detected (e.g., bydropping the traffic, etc.). In this way, mitigator 304 may determinewhether DDoS detector/DOTS client 304 failed to detect the attack (e.g.,issued a false negative), based on its own analysis of the traffic aftermitigation was requested by a source other than DOTS client 304.

As shown in FIG. 5C, mitigator 304 may send feedback 506 to DOTS server306 regarding the mitigated attack, thereby confirming to DOTS server306 that detector/client 304 issued a false negative with respect to thetraffic. In turn, DOTS server 306 may send feedback 506 to client 304thereby informing client 304 that it failed to report the attack. Inaddition, in various embodiments, mitigator 304 may take similar actionsas in the case of a false positive, to cause DDoS detector/DOTS client304 to update its attack detection model. For example, mitigator 304 mayupdate the parameters of the machine learning mechanism based on its ownlabeling of the traffic data and using a technique such as stochasticgradient descent. In turn, mitigator 304 may include the updated machinelearning process, its parameters, and/or the labeled data via feedback506 either directly to client 304 or indirectly through DOTS server 306.

As shown in FIG. 5D, client 304 may use the information in feedback 506to update its machine learning-based attack detection model(s). Forexample, client 304 may use the labeled traffic data to retrain itsmodel. In other cases, client 304 may update the parameters of itsmachine learning process using parameters computed by mitigator 304. Indoing so, client 304 will be better able to avoid another false negativein the future. In further embodiments, the feedback 506 may cause client304 to train and/or install a new attack detection model.

Similar to the information regarding false positives, someimplementations also provide for the sharing of false negativeinformation between entities. For example, DOTS server 306 may providethe labeled traffic data, machine learning parameters, etc. thatresulted from the false negative to other DOTS clients. If the labeledtraffic data is shared outside of the originating entity, the packetsmay be encrypted, to protect the privacy of the sharing entity.

FIG. 6 illustrates an example simplified procedure for the retraining ofan attack detection model, in accordance with various embodimentsherein. Generally, procedure 600 may be performed by any non-generic,specialized device in a network, such as a device configured to act asan attack mitigator (e.g., a DOTS mitigator) or a supervisory deviceover such an action (e.g., a DOTS server). Procedure 600 may start atstep 605 and continues on to step 610 where, as described in greaterdetail, the device receives an attack mitigation request regardingtraffic in the network. In some embodiments, an attack detector may sendthe request indicating that the detector determined that an attack isunderway. In other embodiments, the request may be sent by any otherdevice in the network (e.g., any other DOTS client besides the attackdetector).

At step 615, as detailed above, the device may assess the traffic, inresponse to receiving the mitigation request. In particular, the devicemay be configured to perform more robust or heavyweight attack detectionon the traffic than the source of the mitigation request. For example,the device may perform DPI on the packets to inspect their payloads, actas a TLS proxy to process encrypted traffic, use more powerful attackdetection techniques, or the like, to assess the traffic.

At step 620, the device may determine that an attack detector associatedwith the attack mitigation request incorrectly assessed the traffic, asdescribed in greater detail above. In particular, based on theassessment of the traffic by the device in step 615, the device maydetermine whether the deployed attack detector issued either a falsepositive or a false negative. In the case of a false positive, theattack detector itself may have originated the mitigation request andthe device determines that no actual attack exists. Conversely, in thecase of a false negative, the device may receive the mitigation requestfrom a source other than that of the attack detector and the deviceconfirms that an attack is present.

At step 625, as detailed above, the device may cause an update to anattack detection model of the attack detector, in response todetermining that the attack detector incorrectly assessed the traffic.In some cases, the device may send its labeled traffic data to theattack detector, to allow the attack detector to retrain or update itsown model. In further embodiments, the device may compute new machinelearning parameters for the attack detector and provide these parametersto the attack detector. Procedure 600 then ends at step 630.

It should be noted that while certain steps within procedure 600 may beoptional as described above, the steps shown in FIG. 6 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular ordering of the stepsis shown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, automatically retrainmachine learning-based DDoS attack detectors, to reduce false positivesand false negatives. In addition, the techniques herein can be used withDDoS detectors and mitigators from a variety of different vendors.Further, the techniques herein leverage machine learning, to moreeffectively make use of limited resources and perform decryption of thepackets, which in turn can be used to improve the efficacy of themachine learning process.

While there have been shown and described illustrative embodiments thatprovide for the automatic retraining of machine learning attackdetectors, it is to be understood that various other adaptations andmodifications may be made within the spirit and scope of the embodimentsherein. For example, while certain embodiments are described herein withrespect to using certain mechanisms for purposes of attack detection,the models are not limited as such and may be used for other functions,in other embodiments. In addition, while certain protocols are shown,such as BGP, other suitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: receiving, at a device in anetwork, an attack mitigation request regarding traffic in the network,wherein the device is a Distributed Denial of Service (DDoS) Open ThreatSignaling (DOTS) attack mitigator, causing, by the device, an assessmentof the traffic, in response to the attack mitigation request;determining, by the device, that an attack detector associated with theattack mitigation request incorrectly assessed the traffic, based on theassessment of the traffic, wherein the attack detector is a DistributedDenial of Service (DDoS) Open Threat Signaling (DOTS) client; andcausing, by the device, an automatic update to an attack detection modelof the attack detector, in response to determining that the attackdetector incorrectly assessed the traffic.
 2. The method as in claim 1,wherein the attack mitigation request was initiated by the attackdetector, and wherein determining that the attack detector incorrectlyassessed the traffic comprises: determining, by the device, that theattack mitigation request indicates that the assessment of the trafficby the attack detector was a false positive, based on the assessment ofthe traffic.
 3. The method as in claim 1, wherein the attack mitigationrequest was initiated by a destination of the traffic or via a userinterface, and wherein determining that the attack detector incorrectlyassessed the traffic comprises: determining, by the device, that theattack mitigation request indicates that the assessment of the trafficby the attack detector was a false negative.
 4. The method as in claim1, wherein causing, by the device, the update to the attack detectionmodel comprises: associating, by the device, one or more labels with thetraffic, based on the assessment of the traffic by the device; andproviding, by the device, the one or more labels to the attack detectorto update the attack detection model using the one or more labels. 5.The method as in claim 1, wherein causing, by the device, the update tothe attack detection model comprises: determining, by the device,updated parameters for the attack detection model, based on theassessment of the traffic by the device; and providing, by the device,the updated parameters to the attack detector.
 6. The method as in claim5, wherein the updated parameters for the attack detection model aredetermined using stochastic gradient descent.
 7. The method as in claim1, wherein the attack mitigation request is received via a DistributedDenial of Service (DDoS) Open Threat Signaling (DOTS) server.
 8. Anapparatus, comprising: one or more network interfaces to communicatewith a network; a processor coupled to the network interfaces andconfigured to execute one or more processes; and a memory configured tostore a process executable by the processor, the process when executedoperable to: receive an attack mitigation request regarding traffic inthe network, wherein the apparatus is a Distributed Denial of Service(DDoS) Open Threat Signaling (DOTS) attack mitigator; cause anassessment of the traffic, in response to the attack mitigation request;determine that an attack detector associated with the attack mitigationrequest incorrectly assessed the traffic, based on the assessment of thetraffic, wherein the attack detector is a Distributed Denial of Service(DDoS) Open Threat Signaling (DOTS) client; and cause an automaticupdate to an attack detection model of the attack detector, in responseto determining that the attack detector incorrectly assessed thetraffic.
 9. The apparatus as in claim 8, wherein the attack mitigationrequest was initiated by the attack detector, and wherein the apparatusdetermines that the attack detector incorrectly assessed the traffic by:determining that the attack mitigation request indicates that theassessment of the traffic by the attack detector was a false positive,based on the assessment of the traffic.
 10. The apparatus as in claim 8,wherein the attack mitigation request was initiated by a destination ofthe traffic or via a user interface, and wherein the apparatusdetermines that the attack detector incorrectly assessed the traffic by:determining that the attack mitigation request indicates that theassessment of the traffic by the attack detector was a false negative.11. The apparatus as in claim 8, wherein the apparatus causes the updateto the attack detection model by: associating one or more labels withthe traffic, based on the assessment of the traffic by the apparatus;and providing the one or more labels to the attack detector to updatethe attack detection model using the one or more labels.
 12. Theapparatus as in claim 8, wherein the apparatus causes the update to theattack detection model by: determining updated parameters for the attackdetection model, based on the assessment of the traffic by theapparatus; and providing the updated parameters to the attack detector.13. The apparatus as in claim 12, wherein the updated parameters for theattack detection model are determined using stochastic gradient descent.14. The apparatus as in claim 8, wherein the attack mitigation requestis received via a Distributed Denial of Service (DDoS) Open ThreatSignaling (DOTS) server.
 15. A tangible, non-transitory,computer-readable medium storing program instructions that cause adevice in a network to execute a process comprising: receiving, at thedevice, an attack mitigation request regarding traffic in the network,wherein the device is a Distributed Denial of Service (DDoS) Open ThreatSignaling (DOTS) attack mitigator; causing an assessment of the traffic,in response to the attack mitigation request; determining, by thedevice, that an attack detector associated with the attack mitigationrequest incorrectly assessed the traffic, based on the assessment of thetraffic, wherein the attack detector is a Distributed Denial of Service(DDoS) Open Threat Signaling (DOTS) client; and causing, by the device,an automatic update to an attack detection model of the attack detector,in response to determining that the attack detector incorrectly assessedthe traffic.
 16. The computer-readable medium as in claim 15, whereinthe attack mitigation request indicates that the traffic is associatedwith a Distributed Denial of Service (DDoS) attack.
 17. Thecomputer-readable medium as in claim 15, wherein the attack mitigationrequest is received via a Distributed Denial of Service (DDoS) OpenThreat Signaling (DOTS) server.
 18. The computer-readable medium as inclaim 15, wherein the process is further configured to cause the updateto the attack detection model by: associating one or more labels withthe traffic, based on the assessment of the traffic by the apparatus;and providing the one or more labels to the anomaly detector to updatethe attack detection model using the one or more labels.
 19. Thecomputer-readable medium as in claim 15, wherein the process is furtherconfigured to cause the update to the attack detection model by:determining updated parameters for the attack detection model, based onthe assessment of the traffic by the apparatus; and providing theupdated parameters to the attack detector.
 20. The computer-readablemedium as in claim 19, wherein the updated parameters for the attackdetection model are determined using stochastic gradient descent.